Coincidentally, generating the named entities (names, locations, dates, etc), is one of the most critical challenges in news image captioning.
We analyze the captions and the corresponding articles on three popular benchmarks, GoodNews [1]}, NY800KTimes [2]}, VisualNews [3]}, and find that 58.7\(\%\)  – 74.5\(\%\)  of the named entities present in captions also appear in the articles.
This shows that the key information of a news caption, namely the named entities, can often be directly derived from the associated news article. However, due to the length and abundance of other information in the full articles, it is challenging to uncover the key entities. Inspired by this, we explore how to select relevant yet sufficient context from the news article to assist with the news image caption generation.
